Data Monetization Framework

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A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.

DATA MONETIZATION
AN APPROACH
We believe an effective data monetization strategy necessitates enterprise to perceive data as an
ever evolving asset enabling creation of new information and insights.
30% of businesses will begin
directly or indirectly monetizing
their information assets via
bartering or selling
By Start of 2017…
80% of Chief Data Officers will
strive to maximize the value of
information and to minimize its
risks
Analytics will be delivered via API
Prescriptive analytics built on
cognitive computing, delivering
$60+ Billion in annual savings
By 2020…
FRAMEWORK - ELEMENTS
Explore Data – Unstructured &
Structured, both in terms of data sources
– Internal & External
Data Services
Industry Specific Insights
Cross Industry Insights
Horizontal Insights
Data Product
Internally generated data, customer or
client generated data, collaborative public,
privately acquired data
Data Classification
Differentiation
Cost to Value
Speed to Value
Value for Buyer
Understand Market Opportunity Identify Business Model
Data Privacy, Controls &
Regulations
Identify Key data Analytic Signals Data Delivery Focus
Partnerships, Alliance and
Network Effect
Enterprises are looking for innovative ways to take the data they have
generated and build revenue model around selling insights to
different third parties. We present a framework that will help
enterprises to transform the data asset into tangible revenue
While working within
confines of client’s data
platforms, we understand
the data structures, we can
apply this framework for
building new revenue
models through Insights
(and not just data)
Why Us?

DATA MONETIZATION
AN APPROACH
Implementing Data Monetization requires transformational approach for data sourcing,
transformation & delivery of Insights through APIs for Internal & External Business Groups
APACHE NIFI FOR REAL TIME DATA INGESTION
KAFKA FOR REAL TIME MESSAGING
APACHE SPARK FOR REAL TIME STREAMING
APACHE KYLIN FOR EXTREME OLAP – REAL TIME DATA CUBES
IN MEMORY ANALYTICS BY ALLUXIO, ROCKS DB, APACHE ARROW
INTRODUCE AGILITY IN DESIGN THROUGH MICROSERVICES
INSIGHT GENERATION THROUGH APACHE SYSTEM ML, GOOGLE TENSORFLOW,
MICROSOFT DMTK, AMAZON DSSTNE, SPARK ML
TAKE THE SET UP TO CLOUD – KEEP MODELS ON CLOUD, DATA ON PREMISE
TECHNICAL APPROACH THROUGH BIG DATA & ANALYTICS

Data monetization is a cross-functional discipline that draws from best practices in Enterprise Data Management (EDM), technology, legal engineering, and finance to leverage data to increase revenues, reduce costs, and manage risk. EDM programs have generally found it extremely difficult to get senior management buy-in the absence of regulatory pressures or the fear of a data breach. Data monetization is an approach to drive quantifiable business benefits from data and information. This bottom-line driven approach is key to generating business adoption with stakeholders. This session will review the key aspects of data monetization: • Introduction to Data Monetization • Identify Stakeholders • Build Inventory of Use Cases • Develop Business Cases • Execute Initiatives • Realize Business Benefits • Legal Engineering and Regulatory Compliance • Data Marketplace

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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.

GraphRAG is All You need? LLM & Knowledge Graph

Guy Korland

Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs. 1. Unifying Large Language Models and Knowledge Graphs: A Roadmap. https://arxiv.org/abs/2306.08302 2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

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Essentials of Automations: Optimizing FME Workflows with Parameters

Safe Software

Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place. Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects. Here’s what you’ll gain: - Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows. - Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy. - Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency. - Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity. We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic. Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.

Elevating Tactical DDD Patterns Through Object Calisthenics

Dorra BARTAGUIZ

After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!

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1. DATA MONETIZATION AN APPROACH We believe an effective data monetization strategy necessitates enterprise to perceive data as an ever evolving asset enabling creation of new information and insights. 30% of businesses will begin directly or indirectly monetizing their information assets via bartering or selling By Start of 2017… 80% of Chief Data Officers will strive to maximize the value of information and to minimize its risks Analytics will be delivered via API Prescriptive analytics built on cognitive computing, delivering $60+ Billion in annual savings By 2020… FRAMEWORK - ELEMENTS Explore Data – Unstructured & Structured, both in terms of data sources – Internal & External Data Services Industry Specific Insights Cross Industry Insights Horizontal Insights Data Product Internally generated data, customer or client generated data, collaborative public, privately acquired data Data Classification Differentiation Cost to Value Speed to Value Value for Buyer Understand Market Opportunity Identify Business Model Data Privacy, Controls & Regulations Identify Key data Analytic Signals Data Delivery Focus Partnerships, Alliance and Network Effect Enterprises are looking for innovative ways to take the data they have generated and build revenue model around selling insights to different third parties. We present a framework that will help enterprises to transform the data asset into tangible revenue While working within confines of client’s data platforms, we understand the data structures, we can apply this framework for building new revenue models through Insights (and not just data) Why Us?

2. DATA MONETIZATION AN APPROACH Implementing Data Monetization requires transformational approach for data sourcing, transformation & delivery of Insights through APIs for Internal & External Business Groups APACHE NIFI FOR REAL TIME DATA INGESTION KAFKA FOR REAL TIME MESSAGING APACHE SPARK FOR REAL TIME STREAMING APACHE KYLIN FOR EXTREME OLAP – REAL TIME DATA CUBES IN MEMORY ANALYTICS BY ALLUXIO, ROCKS DB, APACHE ARROW INTRODUCE AGILITY IN DESIGN THROUGH MICROSERVICES INSIGHT GENERATION THROUGH APACHE SYSTEM ML, GOOGLE TENSORFLOW, MICROSOFT DMTK, AMAZON DSSTNE, SPARK ML TAKE THE SET UP TO CLOUD – KEEP MODELS ON CLOUD, DATA ON PREMISE TECHNICAL APPROACH THROUGH BIG DATA & ANALYTICS

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